# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD 3-Clause license found in the
# LICENSE file in the root directory of this source tree.
from itertools import chain
from operator import getitem
from typing import Callable, Dict, Optional, Set, Tuple, Type, Union

import torch
import torch.nn.functional as F
from torch import nn
from torch.fx import symbolic_trace
from torch.nn.utils import parametrize

from torchao.prototype.sparsity import BaseSparsifier

from .match_utils import MatchAllNode, apply_match
from .parametrization import BiasHook, FakeStructuredSparsity, module_contains_param
from .prune_functions import (
    prune_conv2d,
    prune_conv2d_activation_conv2d,
    prune_conv2d_activation_pool_conv2d,
    prune_conv2d_conv2d,
    prune_conv2d_pool_activation_conv2d,
    prune_conv2d_pool_flatten_linear,
    prune_linear,
    prune_linear_activation_linear,
    prune_linear_linear,
    prune_lstm_output_layernorm_linear,
    prune_lstm_output_linear,
)


def _get_supported_structured_pruning_modules():
    SUPPORTED_STRUCTURED_PRUNING_MODULES = {  # added to config if None given
        nn.Linear,
        nn.Conv2d,
        nn.LSTM,
    }
    return SUPPORTED_STRUCTURED_PRUNING_MODULES


def _get_supported_activation_functions():
    SUPPORTED_ACTIVATION_FUNCTIONS = {
        F.relu,
        F.rrelu,
        F.hardtanh,
        F.relu6,
        F.sigmoid,
        F.hardsigmoid,
        F.tanh,
        F.silu,
        F.mish,
        F.hardswish,
        F.elu,
        F.celu,
        F.selu,
        F.hardshrink,
        F.leaky_relu,
        F.logsigmoid,
        F.softplus,
        F.prelu,
        F.softsign,
        F.tanhshrink,
        F.gelu,
        F.dropout,
    }
    return SUPPORTED_ACTIVATION_FUNCTIONS


def _get_supported_activation_modules():
    SUPPORTED_ACTIVATION_MODULES = {
        nn.ReLU,
        nn.RReLU,
        nn.Hardtanh,
        nn.ReLU6,
        nn.Sigmoid,
        nn.Hardsigmoid,
        nn.Tanh,
        nn.SiLU,
        nn.Mish,
        nn.Hardswish,
        nn.ELU,
        nn.CELU,
        nn.SELU,
        nn.Hardshrink,
        nn.LeakyReLU,
        nn.LogSigmoid,
        nn.Softplus,
        nn.PReLU,
        nn.Softsign,
        nn.Tanhshrink,
        nn.GELU,
        nn.Dropout,
    }
    return SUPPORTED_ACTIVATION_MODULES


def _get_default_structured_pruning_patterns() -> Dict[
    Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
    Callable[..., None],
]:
    """
    Returns the patterns for conv2d / linear conversion for each element in the activation functions/modules defined above.
    """
    patterns: Dict[
        Tuple[Union[Type[nn.Module], Callable, MatchAllNode, str], ...],
        Callable[..., None],
    ] = {
        # linear -> linear
        (nn.Linear, "output"): prune_linear,
        (nn.Linear, nn.Linear): prune_linear_linear,
        # conv2d -> conv2d
        (nn.Conv2d, "output"): prune_conv2d,
        (nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
        # TODO LSTM Structured pruning does not support returned state currently.
        # Should find a way to explicitly match getitem(0) instead of getitem.
        # This will also require changing the pruning function.
        # lstm -> getitem(0) -> linear
        (nn.LSTM, getitem, nn.Linear): prune_lstm_output_linear,
        # lstm -> getitem(0) -> layernorm -> linear
        (nn.LSTM, getitem, nn.LayerNorm, nn.Linear): prune_lstm_output_layernorm_linear,
    }

    for activation in chain(
        _get_supported_activation_functions(), _get_supported_activation_modules()
    ):
        patterns.update(
            {
                # linear -> activation -> linear
                (nn.Linear, activation, nn.Linear): prune_linear_activation_linear,
                # conv2d -> activation -> conv2d
                (nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
                # conv2d -> activation -> pool -> conv2d
                (
                    nn.Conv2d,
                    activation,
                    nn.AvgPool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    F.avg_pool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    nn.MaxPool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                (
                    nn.Conv2d,
                    activation,
                    F.max_pool2d,
                    nn.Conv2d,
                ): prune_conv2d_activation_pool_conv2d,
                # conv2d -> pool -> activation -> conv2d
                (
                    nn.Conv2d,
                    nn.AvgPool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    F.avg_pool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    nn.MaxPool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                (
                    nn.Conv2d,
                    F.max_pool2d,
                    activation,
                    nn.Conv2d,
                ): prune_conv2d_pool_activation_conv2d,
                # conv2d -> adaptive pool -> flatten -> linear
                (
                    nn.Conv2d,
                    nn.AdaptiveAvgPool2d,
                    nn.Flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveAvgPool2d,
                    torch.flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveMaxPool2d,
                    nn.Flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
                (
                    nn.Conv2d,
                    nn.AdaptiveMaxPool2d,
                    torch.flatten,
                    nn.Linear,
                ): prune_conv2d_pool_flatten_linear,
            }
        )
    return patterns


class BaseStructuredSparsifier(BaseSparsifier):
    r"""Base class for structured pruning.

    Abstract methods that need to be implemented:
        - update_mask: Function to compute a new mask for all keys in the
            `groups` attribute.

    Args:
        - defaults [dict]: default configurations will be attached to the
            configuration. Only the keys that don't exist in the `config` will
            be updated.
    """

    def __init__(self, defaults, patterns=None):
        super().__init__(defaults)
        if patterns is None:
            patterns = _get_default_structured_pruning_patterns()
        self.patterns = patterns

    def make_config_from_model(
        self,
        model: nn.Module,
        SUPPORTED_MODULES: Optional[Set[Type]] = None,
    ) -> None:
        if SUPPORTED_MODULES is None:
            SUPPORTED_MODULES = _get_supported_structured_pruning_modules()
        super().make_config_from_model(model, SUPPORTED_MODULES=SUPPORTED_MODULES)

    def _prepare(self, *args, **kwargs) -> None:
        r"""This function will attach the FakeStructuredSparsity parameterizations
        and BiasHooks at the appropriate points in the model.
        """
        for config in self.groups:
            module = config["module"]
            tensor_name = config["tensor_name"]
            parametrization = config.get("parametrization", FakeStructuredSparsity)
            tensor = getattr(module, tensor_name)

            mask = config.get(
                "mask",
                torch.ones(tensor.shape[0], dtype=torch.bool, device=tensor.device),
            )
            self.state[config["tensor_fqn"]]["mask"] = mask
            parametrize.register_parametrization(
                module, tensor_name, parametrization(mask)
            )

            # if linear / conv, we add in bias hooks
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                prune_bias = config.get("prune_bias", True)
                if module.bias is not None:
                    module.register_parameter(
                        "_bias", nn.Parameter(module.bias.detach())
                    )
                    module.bias = None
                    module.prune_bias = prune_bias

                module.register_forward_hook(
                    BiasHook(module.parametrizations.weight[0], prune_bias)
                )

    def prune(self) -> None:
        r"""
        This function will FX symbolically trace the model and then find instances of the patterns
        defined in self.patterns (by default SUPPORTED_STRUCTURED_PRUNING_PATTERNS ).

        For each pattern, it will apply to corresponding conversion function, which will modify the output
        and input size expected by the modules within the pattern
        """

        self.traced = symbolic_trace(self.model)
        modules = dict(self.traced.named_modules())

        # Right now we check for matches simply by iterating across all the patterns
        # if this is slow we can store patterns in a trie-structure and modify this code for faster lookup
        for node in self.traced.graph.nodes:
            for pattern, convert_fn in self.patterns.items():
                matched = apply_match(modules, pattern, node, [])
                if matched is None:
                    continue

                first_module = modules.get(node.target)
                # check if first module exists and has appropriate parameterization, otherwise skip
                if (
                    first_module is not None
                    and parametrize.is_parametrized(first_module)
                    and module_contains_param(first_module, FakeStructuredSparsity)
                ):
                    convert_block = []
                    for node in matched:
                        if node.op == "call_module":
                            convert_block.append(modules.get(node.target))
                        elif node.op == "call_function":
                            convert_block.append(node.target)
                    convert_fn(*convert_block)

        for module in self.traced.modules():
            if module_contains_param(module, FakeStructuredSparsity):
                raise Exception(
                    f"Error: {module} still contains FakeStructuredSparsity parametrizations!"
                )

        self.traced.graph.lint()
        self.traced.recompile()
        return self.traced
